Data Management and Analysis Core (DMAC)

数据管理和分析核心 (DMAC)

基本信息

批准号：
10352515
负责人：
Harrison Dekker
金额：
$ 23.06万
依托单位：
UNIVERSITY OF RHODE ISLAND
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10352515
关键词：
Address Adopted Bayesian Modeling Biometry Code Collaborations Communities Computing Methodologies Data Data Analyses Data Analytics Data Science Development Discipline Disease Documentation Ensure Environmental Health FAIR principles Feedback Goals Growth Health High Performance Computing Information Storage Infrastructure Link Machine Learning Mass Spectrum Analysis Mathematics Metadata Methodology Methods Mission Modeling Modernization Monitor National Institute of Environmental Health Sciences Outcome Policy Maker Preparation Procedures Process Protocols documentation Quality Control Reproducibility Research Research Design Research Personnel Research Project Grants Resource Sharing Resources Running Science Series Signal Recognition Particle Source Statistical Data Interpretation Statistical Methods Structure Time Training Training Support Universities Work community engagement complex data computing resources cost data exploration data integration data interoperability data management data quality data repository data reuse data science infrastructure data sharing data sharing networks data visualization design experience flexibility improved innovation member method development open data power analysis quality assurance repository skills sound statistics tool user-friendly

项目摘要

PROJECT SUMMARY/ABSTRACT – DATA MANAGEMENT AND ANALYSIS CORE (DMAC) To understand the link between PFAS exposure and disease, there is a need for data integration from a broad range of scientific disciplines and for researchers to acknowledge the importance of the entire lifecycle of the data in a context beyond their immediate research objective. The long-term goal is to establish a data science infrastructure that promotes best practice, i.e., high-quality data that are Findable, Accessible, Interoperable, and Reusable (FAIR), and that will be easily applicable to other interdisciplinary team projects. DMAC’s overall objective is to work closely with all STEEP project members and equip them with low-cost, user-friendly, FAIR- integrated processes, as well as cutting-edge statistical and computing methods. Guided by the team’s experience, DMAC will pursue four specific aims: (i) develop, coordinate, and monitor a user-friendly, easily- accessible infrastructure and processes for creating, storing, and sharing data and metadata, irrespective of size, both internally and publicly, (ii) address metadata needs across all STEEP research data products, (iii) provide integrative methodological and computational support, as well as develop mission-oriented methods, and (iv) develop standards for and provide data quality assurance and quality control (QA/QC) across STEEP projects. The approach is innovative because it departs from the status quo by providing: (i) an easy-to- implement, modern, and integrative data management infrastructure that is compliant with all FAIR principles and QA/QC, (ii) cutting-edge statistical methods (e.g., causal inference, Bayesian, and time series models) to draw mathematically-precise inferences from complex data structures (e.g., non-randomized, longitudinal), and (iii) high-performance computing resources. The proposed research is significant because it is expected to advance and expand the use of FAIR-compliant research in the field of environmental health. Ultimately, such practice has the potential to inform policy makers with precise and reliable findings and help reduce the reproducibility crisis. STEEP’s DMAC will pursue these goals via these Specific Aims: Specific Aim 1: Develop and support infrastructure and processes for sharing data and metadata Specific Aim 2: Address metadata needs across all STEEP research data products: Specific Aim 3: Provide integrative statistical support Specific Aim 4: Develop standards for and provide data quality assurance and quality control (QA/QC) across STEEP research projects

项目摘要/摘要 – 数据管理和分析核心 (DMAC) 为了了解 PFAS 暴露与疾病之间的联系，需要整合广泛的数据范围广泛的科学学科，并让研究人员认识到整个生命周期的重要性长期目标是建立数据科学。促进最佳实践的基础设施，即可查找、可访问、可互操作和的高质量数据可重复使用（公平），并且可以轻松应用于 DMAC 的其他跨学科团队项目。目标是与所有 STEEP 项目成员密切合作，为他们配备低成本、用户友好、公平的集成的流程以及尖端的统计和计算方法由团队指导。根据经验，DMAC 将追求四个具体目标：(i) 开发、协调和监控用户友好、易于- 用于创建、存储和共享数据和元数据的可访问基础设施和流程，无论内部和公开的规模，(ii) 满足所有 STEEP 研究数据产品的元数据需求，(iii) 提供综合方法和计算支持，并开发面向任务的方法， (iv) 为 STEEP 制定标准并提供数据质量保证和质量控制 (QA/QC) 该方法具有创新性，因为它脱离了现状，提供了：(i) 易于操作的方法。实施符合所有 FAIR 原则的现代化综合数据管理基础设施和 QA/QC，(ii) 尖端统计方法（例如因果推理、贝叶斯和时间序列模型）从复杂的数据结构（例如非随机、纵向）中得出数学上精确的推论，以及 (iii) 高性能计算资源。拟议的研究意义重大，因为它预计将最终，推进并扩大符合 FAIR 标准的研究在环境健康领域的应用。实践有可能为政策制定者提供准确可靠的调查结果，并有助于减少再现性危机。STEEP 的 DMAC 将通过以下具体目标来实现这些目标：具体目标 1：开发和支持共享数据和元数据的基础设施和流程具体目标 2：满足所有 STEEP 研究数据产品的元数据需求：具体目标 3：提供综合统计支持具体目标 4：制定数据质量保证和质量控制标准并提供数据质量保证和质量控制 (QA/QC) 跨 STEEP 研究项目